One of the most common mistakes businesses are making with AI is also one of the most predictable: they are taking the processes they already have and asking where AI can be inserted.
That usually leads to useful ideas. AI can summarize information, draft responses, recommend products, prepare approvals, or help employees find what they need faster. Those are all valid use cases, but they can also keep the conversation too narrow.
The better question is whether the process itself still makes sense.
If you were designing that workflow today, with modern cloud platforms, automation, APIs, and AI available from the beginning, would you build it the same way?
In many cases, the answer is no.
Most Processes Were Built Around Old Constraints
Business processes rarely come from a clean-sheet design. They evolve over time.
An approval gets added because of a problem that happened years ago. A spreadsheet appears because two systems do not communicate well. Someone becomes responsible for moving data from one department to another. A report gets built because the underlying information is difficult to access. A workaround that was supposed to be temporary becomes part of the process.
Over time, all of that becomes normal.
The process works, but often only because people know the exceptions, the side conversations, the manual checks, and the unofficial steps required to keep it moving.
This is what I think of as process debt.
Most companies are familiar with technical debt: old code, legacy integrations, outdated architecture, or customizations that become harder to support over time. Process debt is similar. It is the accumulation of manual steps, unnecessary approvals, duplicated effort, organizational handoffs, and workarounds that were created to solve constraints that may no longer exist.
AI is starting to expose that debt.
Faster Is Not Always Better
A lot of the current AI conversation is focused on productivity: helping someone complete a task faster.
That is useful, but there is a difference between making a task faster and removing unnecessary work from the process entirely.
Take a complex quote-to-order process. A salesperson may gather customer requirements, enter information into CRM, configure a product, request pricing approval, wait for engineering input, revise the quote, create a proposal, send it to the customer, and eventually hand everything to another team for order processing.
There are plenty of opportunities to insert AI into that workflow. AI could summarize the requirements, recommend products, draft the approval request, generate proposal content, or surface similar historical deals.
But that still assumes the existing workflow is the right workflow.
A more interesting conversation starts when you ask why those handoffs exist in the first place.
Could customer requirements be evaluated automatically against product and configuration rules? Could pricing approval become exception-based instead of requiring approval on every transaction? Could engineering only be involved when a configuration falls outside known parameters? Could order validation happen throughout the quoting process instead of at the end?
Once you start asking those questions, you are no longer trying to automate the process you have.
You are redesigning the process you need.
Start With the Outcome, Not the Workflow
This is where I think many technology initiatives need to change.
Instead of beginning with, “Here is our current process. How can we automate it?” start with the business outcome.
Maybe the goal is to reduce quote turnaround time. Maybe it is to reduce order errors, remove manual approvals, improve customer self-service, or free salespeople from chasing internal information.
Once the outcome is clear, work backward.
What would the ideal process look like? Which steps actually create value? Which decisions truly require a person? Which decisions can be handled by business rules? Which decisions could increasingly be handled by AI? Where are employees acting as the connection point between systems simply because the systems themselves are not connected?
Those are the questions that should shape the technology design.
If the process is never challenged, there is a real risk that companies will spend a lot of money making inefficient workflows move faster.
AI Changes the Assumptions
This does not mean every process should be rebuilt around AI, and it certainly does not mean every decision should be automated.
The point is that some of the assumptions behind existing processes are changing.
Historically, certain activities required people because the technology could not interpret unstructured information well. Integrations were expensive. Automation required highly predictable rules. Systems had limited context. Exceptions were difficult to manage.
Those constraints are becoming less absolute.
That creates an opportunity to revisit decisions that may have been made five, ten, or fifteen years ago and ask whether they are still necessary.
The important part is not putting AI everywhere. It is recognizing that AI gives businesses another reason to challenge how work gets done.
The Real Opportunity Is Process Redesign
There will absolutely be value in using AI to help employees work faster. That is already happening.
But I think the larger opportunity will come from companies that are willing to redesign workflows rather than simply adding AI to them.
If five people participate in an approval process today, the goal should not automatically be to help those five people approve something faster. The better question is whether all five people need to participate at all.
If an employee spends hours moving information between systems, the goal should not automatically be to give that employee an AI assistant. The better question is why the information needs to be moved manually in the first place.
If a customer waits three days because work is sitting in different internal queues, the goal should not simply be to make each queue more efficient. The better question is whether the queues still need to exist.
That is the mindset shift.
Modern technology gives businesses an opportunity to stop designing around yesterday’s constraints.
Use AI where it creates value. Automate where it makes sense. But before doing either, challenge the process itself.
Because the biggest opportunity may not be doing what you already do faster.
It may be deciding that you no longer need to do it that way at all.